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Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study

BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative eff...

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Autores principales: Guzzi, Pietro H, Tradigo, Giuseppe, Veltri, Pierangelo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7945976/
https://www.ncbi.nlm.nih.gov/pubmed/33629957
http://dx.doi.org/10.2196/18933
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author Guzzi, Pietro H
Tradigo, Giuseppe
Veltri, Pierangelo
author_facet Guzzi, Pietro H
Tradigo, Giuseppe
Veltri, Pierangelo
author_sort Guzzi, Pietro H
collection PubMed
description BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. OBJECTIVE: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. METHODS: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. RESULTS: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. CONCLUSIONS: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations.
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spelling pubmed-79459762021-03-12 Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study Guzzi, Pietro H Tradigo, Giuseppe Veltri, Pierangelo JMIR Med Inform Original Paper BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. OBJECTIVE: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. METHODS: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. RESULTS: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. CONCLUSIONS: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations. JMIR Publications 2021-03-09 /pmc/articles/PMC7945976/ /pubmed/33629957 http://dx.doi.org/10.2196/18933 Text en ©Pietro H Guzzi, Giuseppe Tradigo, Pierangelo Veltri. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 09.03.2021. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Guzzi, Pietro H
Tradigo, Giuseppe
Veltri, Pierangelo
Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title_full Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title_fullStr Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title_full_unstemmed Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title_short Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study
title_sort regional resource assessment during the covid-19 pandemic in italy: modeling study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7945976/
https://www.ncbi.nlm.nih.gov/pubmed/33629957
http://dx.doi.org/10.2196/18933
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